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Unlike other algorithms like linear regressions ,KNN doesn't seems to perform any calculation in the training phase. Like in case of linear regressions it finds the coefficients in the training phase.But what about KNN?
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Introduction. Lazy learning algorithm − KNN is a lazy learning algorithm because it does not have a specialized training phase and uses all the data for training while classification.
In KNN, the training data points get stored, and no learning is performed. Validation data is to check the model performance, and the test data is used for prediction. To select optimal K, plot the error of model (error = 1 — accuracy) on training as well as on the validation dataset.
K-NN is a lazy learner because it doesn't learn a discriminative function from the training data but “memorizes” the training dataset instead. For example, the logistic regression algorithm learns its model weights (parameters) during training time. In contrast, there is no training time in K-NN.
The model representation for KNN is the entire training dataset. It is as simple as that. KNN has no model other than storing the entire dataset, so there is no learning required.
During training phase, KNN arranges the data (sort of indexing process) in order to find the closest neighbors efficiently during the inference phase. Otherwise, it would have to compare each new case during inference with the whole dataset making it quite inefficient.
You can read more about it at: https://scikit-learn.org/stable/modules/neighbors.html#nearest-neighbor-algorithms
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